6 accepted papers
Vision-Language Models (VLMs) extend the capabilities of Large Language Models (LLMs) by incorporating visual information, yet they remain vulnerable to jailbreak attacks, especially when processing noisy or corrupted images. Although existing VLMs adopt security measures during training to mitigate…
As Visual Language Models (VLMs) continue to evolve, they have demonstrated increasingly sophisticated logical reasoning capabilities and multimodal thought generation, opening doors to widespread applications. However, this advancement raises serious concerns about content security, particularly wh…
We address the challenge of WiFi-based temporal activity detection and propose an efficient Dual Pyramid Network that integrates Temporal Signal Semantic Encoders and Local Sensitive Response Encoders. The Temporal Signal Semantic Encoder splits feature learning into high and low-frequency componen…
In many real-world applications of Deep Neural Networks (DNNs) in visual recognition, data augmentation stands out as a premier tool for enhancing model robustness. Stemming from the understanding of the common mechanisms of data augmentation methods, we introduce the mask-based "data augmentation b…
As Large Language Models (LLMs) become increasingly influential in reasoning tasks, ensuring their trustworthiness and introspective self-awareness is critical. This research introduces the Think-Solve-Verify (TSV) framework, an innovative strategy tailored to explore LLMs’ trustworthiness, introspe…
In the realm of visual recognition, data augmentation stands out as a pivotal technique to amplify model robustness. Yet, a considerable number of existing methodologies lean heavily on heuristic foundations, rendering their intrinsic mechanisms ambiguous. This manuscript takes both a theoretical an…